Modeling Mechanical Properties of FSW Thick Pure Copper Plates and Optimizing It Utilizing Artificial Intelligence Techniques

A. Azizi, A. V. Barenji, R. V. Barenji, M. Hashemipour
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引用次数: 6

Abstract

Friction stir welding (FSW) is an innovative solid state joining technique and has been employed in aerospace, rail, automotive and marine industries for joining aluminum, magnesium, zinc and copper alloys. In this process, parameters play a major role in deciding the weld quality these parameters. Using predictive modelling for mechanical properties of FSW not only reduce experiments but also is created standard model for predict outcomes. Therefore, this paper is undertaken to develop a model to predict the microstructure and mechanical properties of FSW. The proposed model is based on Ring Probabilistic logic Neural Network (RPLNN) and optimize it utilizing Genetic Algorithms (GA). The simulation results show that performance of the RPLNN algorithm with utilizing Genetic Algorithm optimizing technique compared to real data is reliable to deal with function approximation problems, and it is capable of achieving a solution in few convergence time steps with powerful and reliable results.
FSW纯铜厚板力学性能建模及人工智能优化
搅拌摩擦焊(FSW)是一种创新的固态连接技术,已应用于航空航天、铁路、汽车和船舶工业中,用于连接铝、镁、锌和铜合金。在此过程中,参数对焊接质量起着主要的决定作用。采用预测模型对摩擦摩擦器力学性能进行预测,不仅减少了试验次数,而且为预测结果建立了标准模型。因此,本文致力于建立一个预测摩擦焊微观结构和力学性能的模型。该模型基于环概率逻辑神经网络(RPLNN),并利用遗传算法对其进行优化。仿真结果表明,与实际数据相比,采用遗传算法优化技术的RPLNN算法在处理函数逼近问题时性能可靠,能够在较短的收敛时间步内得到解,结果强大可靠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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